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Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis and have implications for non-relapsing biological progression. In recent years, the discovery of innovative MRI and PET-derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with multiple sclerosis, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted and T2-weighted scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a consensus statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this consensus statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com. Elements of this work were written by employees of the US Government.)

Pelizaeus-Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin-modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas-based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single-center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1-weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2-weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann-Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA-based registration, followed by multimodal, and T1w-based registration (SSIM FA  = 0.67 ± 0.04 vs. SSIM multimodal  = 0.60 ± 0.03 vs. SSIM T1  = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWF PMD  = 0.0267 ± 0.021 vs. MWF controls  = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA-based registration pipeline, providing an alternative to conventional T1w-based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas-based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Objectives: This study aimed to verify the accuracy of clinical protocols for the diagnosis of disc displacement (DD) compared with MRI, considering examiners' calibration., Methods: PubMed, Cochrane (Central), Scopus, Web of Science, LILACS, Embase, Science Direct, Google Scholar, and DANS EASY Archive databases were searched. Two reviewers independently screened and selected the studies. A meta-analysis was conducted using the R Statistical software. Results are shown using sensitivity and specificity, and 95% confidence intervals., Results: Of the 20 studies included in the systematic review, only three were classified as low risk of bias. Seventeen studies were included in the meta-analysis. Compared to MRI, clinical protocols showed overall sensitivity and specificity of 0.75 (0.63-0.83) and 0.73 (0.59-0.84) for DD diagnosis, respectively. For DD with reduction, sensitivity was 0.64 (0.48-0.77) and specificity was 0.72 (0.48-0.87). For DD without reduction, sensitivity was 0.58 (0.39-0.74) and specificity 0.93 (0.83-0.97). Only 8 studies reported examiner calibration when performing clinical and/or MRI evaluation; nevertheless, calibration showed a tendency to improve the diagnosis of DD., Conclusion: The sensitivity and specificity of clinical protocols in the diagnosis of DD are slightly below the recommended values, as well as the studies lack calibration of clinical and MRI examiners. Examiner calibration seems to improve the diagnosis of DD., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Aims: The cerebellum is involved in higher-order mental processing as well as sensorimotor functions. Although structural abnormalities in the cerebellum have been demonstrated in schizophrenia, neuroimaging techniques are not yet applicable to identify them given the lack of biomarkers. We aimed to develop a robust diagnostic model for schizophrenia using radiomic features from T1-weighted magnetic resonance imaging (T1-MRI) of the cerebellum., Methods: A total of 336 participants (174 schizophrenia; 162 healthy controls [HCs]) were allocated to training (122 schizophrenia; 115 HCs) and test (52 schizophrenia; 47 HCs) cohorts. We obtained 2568 radiomic features from T1-MRI of the cerebellar subregions. After feature selection, a light gradient boosting machine classifier was trained. The discrimination and calibration of the model were evaluated. SHapley Additive exPlanations (SHAP) was applied to determine model interpretability., Results: We identified 17 radiomic features to differentiate participants with schizophrenia from HCs. In the test cohort, the radiomics model had an area under the curve, accuracy, sensitivity, and specificity of 0.89 (95% confidence interval: 0.82-0.95), 78.8%, 88.5%, and 75.4%, respectively. The model explanation by SHAP suggested that the second-order size zone non-uniformity feature from the right lobule IX and first-order energy feature from the right lobules V and VI were highly associated with the risk of schizophrenia., Conclusion: The radiomics model focused on the cerebellum demonstrates robustness in diagnosing schizophrenia. Our results suggest that microcircuit disruption in the posterior cerebellum is a disease-defining feature of schizophrenia, and radiomics modeling has potential for supporting biomarker-based decision-making in clinical practice., (© 2024 The Author(s). Psychiatry and Clinical Neurosciences © 2024 Japanese Society of Psychiatry and Neurology.)

· Breast MRI is an essential part of breast imaging. · The recommendations for performing breast MRI have been updated. · A table provides a compact and quick overview. More detailed comments supplement the table.. · The "classic" breast MRI can be performed based on the recommendations. Tips for special clinical questions, such as implant rupture, mammary duct pathology or local lymph node status, are included.. CITATION FORMAT: · Wenkel E, Wunderlich P, Fallenberg E et al. Aktualisierung der Empfehlungen der AG Mammadiagnostik der Deutschen Röntgengesellschaft zur Durchführung der Mamma-MRT. Fortschr Röntgenstr 2024; 196: 939 - 944., Competing Interests: Die Autorinnen/Autoren geben an, dass kein Interessenkonflikt besteht., (Thieme. All rights reserved.)

Computationally expensive data processing in neuroimaging research places demands on energy consumption-and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data. Eight variants of fMRIPrep were run with 257 participants who had completed an fMRI stop signal task (the same data also used in the original validation of fMRIPrep). Some variants led to substantial reductions in carbon emissions without sacrificing data quality: for instance, disabling FreeSurfer surface reconstruction reduced carbon emissions by 48%. We provide six recommendations for minimising emissions without compromising performance. By varying parameters and computational resources, neuroimagers can substantially reduce the carbon footprint of their preprocessing. This is one aspect of our research carbon footprint over which neuroimagers have control and agency to act upon., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Although 7 T MRI research has contributed much to our understanding of multiple sclerosis (MS) pathology, most prior data has come from small, single-center studies with varying methods. In order to truly know if such findings have widespread applicability, multicenter methods and studies are needed. To address this, members of the North American Imaging in MS (NAIMS) Cooperative worked together to create a multicenter collaborative study of 7 T MRI in MS. In this manuscript, we describe the methods we have developed for the purpose of pooling together a large, retrospective dataset of 7 T MRIs acquired in multiple MS studies at five institutions. To date, this group has contributed five-hundred and twenty-eight 7 T MRI scans from 350 individuals with MS to a common data repository, with plans to continue to increase this sample size in the coming years. We have developed unified methods for image processing for data harmonization and lesion identification/segmentation. We report here our initial observations on intersite differences in acquisition, which includes site/device differences in brain coverage and image quality. We also report on the development of our methods and training of image evaluators, which resulted in median Dice Similarity Coefficients for trained raters' annotation of cortical and deep gray matter lesions, paramagnetic rim lesions, and meningeal enhancement between 0.73 and 0.82 compared to final consensus masks. We expect this publication to act as a resource for other investigators aiming to combine multicenter 7 T MRI datasets for the study of MS, in addition to providing a methodological reference for all future analysis projects to stem from the development of this dataset., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Accurate segmentation of chronic stroke lesions from mono-spectral magnetic resonance imaging scans (e.g., T1-weighted images) is a difficult task due to the arbitrary shape, complex texture, variable size and intensities, and varied locations of the lesions. Due to this inherent spatial heterogeneity, existing machine learning methods have shown moderate performance for chronic lesion delineation. In this study, we introduced: (1) a method that integrates transformers' deformable feature attention mechanism with convolutional deep learning architecture to improve the accuracy and generalizability of stroke lesion segmentation, and (2) an ecological data augmentation technique based on inserting real lesions into intact brain regions. Our combination of these two approaches resulted in a significant increase in segmentation performance, with a Dice index of 0.82 (±0.39), outperforming the existing methods trained and tested on the same Anatomical Tracings of Lesions After Stroke (ATLAS) 2022 dataset. Our method performed relatively well even for cases with small stroke lesions. We validated the robustness of our method through an ablation study and by testing it on new unseen brain scans from the Ischemic Stroke Lesion Segmentation (ISLES) 2015 dataset. Overall, our proposed approach of transformers with ecological data augmentation offers a robust way to delineate chronic stroke lesions with clinically relevant accuracy. Our method can be extended to other challenging tasks that require automated detection and segmentation of diverse brain abnormalities from clinical scans., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Advanced imaging, including ultrasonography, computed tomography, and magnetic resonance imaging (MRI), is an integral component to the evaluation and management of ill and injured children in the emergency department. As with any test or intervention, the benefits and potential impacts on management must be weighed against the risks to ensure that high-value care is being delivered. There are important considerations specific to the pediatric patient related to the ordering and interpretation of advanced imaging. This policy statement provides guidelines for institutions and those who care for children to optimize the use of advanced imaging in the emergency department setting and was coauthored by experts in pediatric and general emergency medicine, pediatric radiology, and pediatric surgery. The intent is to guide decision-making where children may access care., (Copyright © 2024 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.)

Standardized MR imaging protocols are important for the diagnosis and monitoring of patients with multiple sclerosis (MS) and the appropriate use of MR imaging in routine clinical practice. Advances in using MR imaging to establish an earlier diagnosis of MS, safety concerns regarding intravenous gadolinium-based contrast agents, and the value of spinal cord MR imaging for diagnostic, prognostic, and monitoring purposes suggest a changing role of MR imaging for the management and care of MS patients. The MR imaging protocol emphasizes 3 dimensional acquisitions for optimal comparison over time., Competing Interests: Disclosure A. Traboulsee and DKB Li “related to this work, has received honoraria from the Consortium of MS Centers for devleoping MRI imaging guideline workshops and presentations” and unrelated to this work A. Traboulsee is the MS Canada Research Chair supported by the MSMRI Research Group at UBC, has received grant or clinical trial funding from MS Canada, Canadian Institute for Health Research, Hilton Foundation, Roche, Sanofi, Biogen, Novartis, Abbvie, EMD Serono, Teva Neuroscience, Chugai, has received honoria for data safety monitoriing from Sanofi, honoria for steering committe membership from Roche, Sanofi, and honoria for speakers bureau or advisory boards from Roche, Sanofi, Biogen, Alexion, EMD Serono. Unrelated to this work, D.K.B. Li is Emeritus Director of the UBC MSMRI Research Group which has received grant support for investigator-initiated studies from Genzyme and Roche. He has given lectures, supported by non-restricted education grants from Academy of Health Care Learning, Biogen, Novartis, Consortium of MS Centers and Sanofi-Genzyme., (Copyright © 2024 Elsevier Inc. All rights reserved.)

This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms. In addition, a 3D fast spoiled gradient recalled echo (FSPGR) sequence was used to investigate several flip angles (FA): 2, 5, 10, 15, 20, 25, and 30 degrees. Nineteen phantom compartments were manually segmented. Different approaches were used to pre-process each image: Bin discretization, Wavelet filter, Laplacian of Gaussian, logarithm, square, square root, and gradient. Overall, 92 first-, second-, and higher-order statistical radiomic features were extracted. ComBat harmonization was also applied to the extracted radiomic features. Finally, the Intraclass Correlation Coefficient (ICC) and Kruskal-Wallis's (KW) tests were implemented to assess the robustness of radiomic features. The number of non-significant features in the KW test ranged between 0-5 and 29-74 for various scanners, 31-91 and 37-92 for three times tests, 0-33 to 34-90 for FAs, and 3-68 to 65-89 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The number of features with ICC over 90% ranged between 0-8 and 6-60 for various scanners, 11-75 and 17-80 for three times tests, 3-83 to 9-84 for FAs, and 3-49 to 3-63 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The use of various scanners, IRs, and FAs has a great impact on radiomic features. However, the majority of scanner-robust features is also robust to IR and FA. Among the effective parameters in MR images, several tests in one scanner have a negligible impact on radiomic features. Different scanners and acquisition parameters using various image pre-processing might affect radiomic features to a large extent. ComBat harmonization might significantly impact the reproducibility of MRI radiomic features., (© 2024. The Author(s).)

Introduction: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a recently identified demyelinating disorder with a diverse clinical spectrum. Diagnosing MOGAD traditionally relies on clinical judgment, highlighting the necessity for precise diagnostic criteria. Banwell et al. proposed criteria, aiming to refine the diagnostic spectrum. This study evaluates these criteria in a real-life cohort, comparing their performance with clinical judgment and describe the cohort of MOGAD patients., Methods: This retrospective study, conducted at Hadassah Medical Center, included 88 patients with MOG-IgG antibodies. Patients with a positive or borderline MOG-IgG antibodies by cell-based assay were included. Demographics, clinical and MRI data were recorded. Cases were divided into definite MOGAD and Non-MOGAD groups as determined by the treating physician. We assessed the sensitivity and specificity of the new criteria in comparison to treating physicians' evaluations. Additionally, we examined clinical differences between the MOGAD and Non-MOGAD groups., Results: We observed a strong concordance (98%) between the new MOGAD criteria and treating physicians' diagnoses. Clinical disparities between MOGAD and Non-MOGAD groups included lower EDSS scores, normal MRI scans, preserved brain volume, negative OCB results, and distinct relapse patterns. Also, compared to relapsing patients, monophasic MOGAD patients have greater brain volume and a lower age at onset., Conclusion: The study demonstrates robust accuracy of new MOGAD criteria, emphasizing their potential to enhance diagnostic precision. Treatment response integration into the MOGAD diagnosis is crucial, as it could aid in distinguishing MOGAD from other demyelinating disorders. Distinct clinical profiles highlight the importance of informed decisions in managing MOGAD and similar disorders., (© 2024. The Author(s).)

Objective: Conventionally, MRI aids in differentiating acute unilateral peripheral vestibulopathy/vestibular neuritis (AUPV/VN) from mimickers. Meanwhile, the diagnostic utility of MRIs dedicated to the inner ear remains to be elucidated for diagnosing AUPV/VN., Methods: We prospectively recruited 53 patients with AUPV/VN (mean age ± SD = 60 ± 15 years, 29 men). Initial MRIs were performed with a standard protocol, and an additional axial 3D-fluid-attenuated inversion recovery (3D-FLAIR) sequence was obtained 4 h after intravenous injection of gadoterate meglumine. Abnormal enhancement was defined as a signal intensity that exceeded the mean + 2SD value on the healthy side. The findings of neurotologic evaluation and MRIs were compared., Results: Overall, the inter-rater agreement for gadolinium enhancement was 0.886 (Cohen's kappa coefficient). Enhancement was observed in 26 patients (49%), most frequently in the vestibule (n = 20), followed by the anterior (n = 12), horizontal (HC, n = 8), posterior canal (n = 5), and superior (n = 3) and inferior (n = 1) vestibular nerves. In multivariable logistic regression analysis, the enhancement was associated with decreased HC gain in video head-impulse tests (p = 0.036), increased interaural difference in ocular vestibular-evoked myogenic potentials (p = 0.001), and a longer onset-to-MRI time span (p = 0.024). The sensitivity and specificity were 92.3% and 81.5%, respectively, with an area under the curve of 0.90 for predicting gadolinium enhancement., Interpretation: Robust gadolinium enhancement was observed on 4-hour-delayed 3D-FLAIR images in nearly half of the patients with AUPV/VN, with a good correlation with the results of neurotologic evaluation. The positivity may be determined by the extent of vestibular deficit, timing of imaging acquisition, and possibly by the underlying etiology causing AUPV/VN. MRIs may aid in delineating the involved structures in AUPV/VN., (© 2024 The Author(s). Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking., Objectives: The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders., Methods: This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search., Results: The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented., Conclusions: The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking., Objectives: The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders., Methods: This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and a focused literature search., Results: The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of a suitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for a structured radiological assessment are presented., Conclusions: The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides a possible basis for the standardisation of the measurement protocols at all clinical centres in Germany., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Introduction: It is important to explore strategies reducing the number of SB cores taken to minimize biopsy-related morbidity and patient's discomfort during biopsy. This study aims to optimize prostate biopsy procedures by reducing the number of systematic biopsy (SB) cores while preserving cancer detection rates in the era of combined biopsy., Patients and Methods: We prospectively recruited patients with ≥1 magnetic resonance imaging (MRI) lesions and they underwent transperineal combined 12-core SB+3-core targeted prostate biopsy (TB, reference standard). New strategy was defined as a laterally 6-core SB+3-core TB. Patients were served as their own control. Detection rates for overall prostate cancer (PCa) and clinically significant PCa (csPCa) were compared among the standard SB, MRI-TB, 6-core SB +3-core TB, and reference standard. Pathology consistency was assessed using the Kappa test., Results: A total of 204 men were included, of which 111 (54.41%) and 92 (45.10%) harbored overall PCa and csPCa. Referenced combined biopsy detected significantly 6.86% (P = .0005) or 4.90% (P = .0044) more csPCa than performing only SB or 3-core TB, but was comparable to the new biopsy strategy. (45.10% vs. 43.14%, P = .1336) Similar results persisted when limiting patients in biopsy-naïve men or stratified by Prostate Imaging Reporting and Data System scores, PSAD, and index lesion parameters. Additionally, performing 6-core SB+3-core TB demonstrated high consistency with reference standard in grade group distribution (Kappa coefficient: 0.952 for all, 0.961 for biopsy-naïve men) and achieved superior sensitivity of 95.7% (All: 95% CI: 89.2%-99.8%) and 96.9% (Biopsy-naïve: 95% CI: 91.1%-99.7%), respectively., Conclusions: The 6-core SB+3-core TB approach maintains expected detection rates while reducing the total core count, offering a promising alternative to the reference standard, which may help to tailor transperineal combined biopsy procedures., Competing Interests: Disclosure All authors in the study declare no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Research on the local hippocampal atrophy for early detection of dementia has gained considerable attention. However, accurately quantifying subtle atrophy remains challenging in existing morphological methods due to the lack of consistent biological correspondence with the complex curving regions like the hippocampal head. Thereby, this article presents an innovative axis-referenced morphometric model (ARMM) that follows the anatomical lamellar organization of the hippocampus, which capture its precise and consistent longitudinal curving trajectory. Specifically, we establish an "axis-referenced coordinate system" based on a 7 T ex vivo hippocampal atlas following its entire curving longitudinal axis and orthogonal distributed lamellae. We then align individual hippocampi by deforming this template coordinate system to target spaces using boundary-guided diffeomorphic transformation, while ensuring that the lamellar vectors adhere to the constraint of medial-axis geometry. Finally, we measure local thickness and curvatures based on the coordinate system and boundary surface reconstructed from vector tips. The morphometric accuracy is evaluated by comparing reconstructed surfaces with those directly extracted from 7 T and 3 T MRI hippocampi. The results demonstrate that ARMM achieves the best performance, particularly in the curving head, surpassing the state-of-the-art morphological models. Additionally, morphological measurements from ARMM exhibit higher discriminatory power in distinguishing early Alzheimer's disease from mild cognitive impairment compared to volume-based measurements. Overall, the ARMM offers a precise morphometric assessment of hippocampal morphology on MR images, and sheds light on discovering potential image markers for neurodegeneration associated with hippocampal impairment., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

In pursuit of cultivating automated models for magnetic resonance imaging (MRI) to aid in diagnostics, an escalating demand for extensive, multisite, and heterogeneous brain imaging datasets has emerged. This potentially introduces biased outcomes when directly applied for subsequent analysis. Researchers have endeavored to address this issue by pursuing the harmonization of MRIs. However, most existing image-based harmonization methods for MRI are tailored for 2D slices, which may introduce inter-slice variations when they are combined into a 3D volume. In this study, we aim to resolve inconsistencies between slices by introducing a pseudo-warping field. This field is created randomly and utilized to transform a slice into an artificially warped subsequent slice. The objective of this pseudo-warping field is to ensure that generators can consistently harmonize adjacent slices to another domain, without being affected by the varying content present in different slices. Furthermore, we construct unsupervised spatial and recycle loss to enhance the spatial accuracy and slice-wise consistency across the 3D images. The results demonstrate that our model effectively mitigates inter-slice variations and successfully preserves the anatomical details of the images during the harmonization process. Compared to generative harmonization models that employ 3D operators, our model exhibits greater computational efficiency and flexibility., Competing Interests: Declaration of competing interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Advanced imaging, including ultrasonography, computed tomography, and magnetic resonance imaging, is an integral component to the evaluation and management of ill and injured children in the emergency department. As with any test or intervention, the benefits and potential impacts on management must be weighed against the risks to ensure that high-value care is being delivered. There are important considerations specific to the pediatric patient related to the ordering and interpretation of advanced imaging. This policy statement provides guidelines for institutions and those who care for children to optimize the use of advanced imaging in the emergency department setting and was coauthored by experts in pediatric and general emergency medicine, pediatric radiology, and pediatric surgery. The intent is to guide decision-making where children may access care., (Copyright © 2024 American College of Radiology, American College of Emergency Physicians, and American Academy of Pediatrics. Published by Elsevier Inc on behalf of American College of Radiology and American College of Emergency Physicians and by American Academy of Pediatrics. All rights reserved.)

Introduction: Recent diagnostic criteria for optic neuritis include T2-hyperintensity of the optic nerve (ON), even without associated contrast enhancement. However, isolated ON-T2-hyperintensity is a nonspecific finding found in any optic neuropathy or severe retinopathy. We applied the 2022 optic neuritis diagnostic criteria to a cohort of patients with noninflammatory optic neuropathy and ON-T2-hyperintensity in at least one eye, to assess the rate of optic neuritis misdiagnosis using these criteria., Methods: Retrospective study of consecutive patients who underwent brain/orbit MRI with/without contrast between 07/01/2019 and 06/30/2022. Patients with ON-T2-hyperintensity in at least one eye were included. The 2022 optic neuritis diagnostic criteria were applied to patients with noninflammatory optic neuropathies who had an ophthalmologic examination available for review., Results: Of 150 patients included, 85/150 had compressive optic neuropathy; 32/150 had glaucoma; 12/150 had papilledema; 8/150 had hereditary (3), radiation-induced (3), nutritional (1), traumatic (1) optic neuropathies (none fulfilled the criteria); 13/150 had ischemic optic neuropathy and 4 fulfilled the criteria as definite optic neuritis due to contrast enhancement of the ON head. Seven additional patients would have satisfied the diagnostic criteria if red flags for alternative diagnoses had been overlooked., Discussion: The application of the 2022 optic neuritis diagnostic criteria in patients with noninflammatory optic neuropathy and ON-T2-hyperintensity in at least one ON resulted in misdiagnosis of optic neuritis in only 4 patients because of ON head enhancement, all with nonarteritic anterior ischemic optic neuropathy. Neuro-ophthalmologic evaluation and exclusion of the ON head as a location in the MRI criteria would have prevented optic neuritis misdiagnosis in our study., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, which brings about associated complexities. This study investigates the potential of deep learning to differentiate between active and inactive lesions in MS using non-contrast FLAIR-type MRI data, presenting a non-invasive alternative to conventional gadolinium-based MRI methods., Methods: The dataset encompasses 9097 lesion images collected from 130 MS patients across four distinct imaging centers, with post-contrast T1-weighted images as the benchmark reference. We initially identified and labeled the lesions and carefully selected corresponding regions of interest (ROIs). These ROIs were employed as inputs for a convolutional neural network (CNN) to predict lesion status. Also, transfer learning was utilized, incorporating 12 pre-trained CNN models. Subsequently, an ensemble technique was applied to 3 of best models, followed by a systematic comparison of the results., Results: Through a 5-fold cross-validation, our custom designed network exhibited an average accuracy of 85 %, a sensitivity of 95 %, a specificity of 75 %, and an AUC value of 0.90. Among the pre-trained models, ResNet50 emerged as the most effective, achieving a specificity of 58 %, an accuracy of 75 %, a sensitivity of 91 %, and an AUC value of 0.81. Our comprehensive evaluations encompassed the receiver operating characteristic curve, precision-recall curve, and confusion matrix analyses., Conclusion: The findings underscore the efficacy of the proposed CNN, trained on FLAIR MRI data ROIs, in accurately discerning active and inactive lesions without reliance on contrast agents. Our multicenter study of 130 patients with diverse imaging devices outperforms the other single-center studies, achieving superior sensitivity and specificity. Unlike studies using multiple modalities, our exclusive use of FLAIR images streamlines the process, and our streamlined approach, excluding conventional pre-processing, demonstrates efficiency. The external validation conducted on diverse datasets, coupled with the analysis of dilated masks, underscores the adaptability and efficacy of our custom CNN model in discerning between active and inactive lesions., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Introduction: Loss of dorsolateral nigral hyperintensity (DNH) on iron-sensitive brain MRI is useful for Parkinson's disease detection. DNH loss could also be of diagnostic value in dementia with Lewy bodies (DLB), an a-synuclein-related pathology. We aim to quantitatively synthesize evidence, investigating the role of MRI, a first-line imaging modality, in early DLB detection and differentiation from other dementias., Methods: Our study was conducted according to the PRISMA statement. MEDLINE, Scopus, Web of Science, and Cochrane Library were searched using the terms like "dementia with Lewy bodies", "dorsolateral nigral hyperintensity", and "MRI". Only English-written peer-reviewed diagnostic accuracy studies were included. We used QUADAS-2 for quality assessment., Results: Our search yielded 363 search results. Three studies were eligible, all with satisfying, high quality. The total population of 227 patients included 63 with DLB and 164 with other diseases (Alzheimer disease, frontotemporal dementia, mild cognitive impairment). Using a univariate random-effects logistic regression model, our meta-analysis resulted in pooled sensitivity, specificity and DOR of 0.82 [0.62; 0.92], 0.79 [0.70; 0.86] and 16.26 ([3.3276; 79.4702], p = 0.0006), respectively, for scans with mixed field strength (1.5 and 3 T). Subgroup analysis of 3 T scans showed pooled sensitivity, specificity and DOR of 0.82 [0.61; 0.93], 0.82 [0.72; 0.89] and 18.36 ([4.24; 79.46], p < 0.0001), respectively., Discussion: DNH loss on iron-sensitive MRI might comprise a supportive biomarker for DLB detection, that could augment the value of the DLB diagnostic criteria. Further evaluation using standardized protocols is needed, as well as direct comparison to other supportive and indicative biomarkers., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Pooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets., (© 2024. The Author(s).)

Cardiovascular magnetic resonance (CMR) and computed tomography (CCT) are advanced imaging modalities that recently revolutionized the conventional diagnostic approach to congenital heart diseases (CHD), supporting echocardiography and often replacing cardiac catheterization. This is the second of two complementary documents, endorsed by experts from the Working Group of the Italian Society of Pediatric Cardiology and the Italian College of Cardiac Radiology of the Italian Society of Medical and Interventional Radiology, aimed at giving updated indications on the appropriate use of CMR and CCT in different clinical CHD settings, in both pediatrics and adults. In this article, support is also given to radiologists, pediatricians, cardiologists, and cardiac surgeons for indications and appropriateness criteria for CMR and CCT in the most referred CHD, following the proposed new criteria presented and discussed in the first document. This second document also examines the impact of devices and prostheses for CMR and CCT in CHD and additionally presents some indications for CMR and CCT exams when sedation or narcosis is needed., (Copyright © 2024 Italian Federation of Cardiology - I.F.C. All rights reserved.)

Purpose: We aim to propose a visual quantitative score for muscle edema in lower limb MRI to contribute to the diagnosis of idiopathic inflammatory myopathy (IIM)., Material and Methods: We retrospectively evaluated 85 consecutive patients (mean age 57.4 ± 13.9 years; 56.5% female) with suspected IIM (muscle weakness and/or persistent hyper-CPK-emia with/without myalgia) who underwent MRI of lower limbs using T2-weighted fast recovery-fast spin echo images and fat-sat T2 echo planar images. Muscle inflammation was evaluated bilaterally in 11 muscles of the thigh and eight muscles of the leg. Edema in each muscle was graded according to a four-point Likert-type scale adding up to 114 points ([11 + 8)] × 3 × 2). Diagnostic accuracy of the total edema score was explored by assessing sensitivity and specificity using the area under the ROC curve. Final diagnoses were made by a multidisciplinary Expert Consensus Panel applying the Bohan and Peter diagnostic criteria whenever possible., Results: Of the 85 included patients, 34 (40%) received a final diagnosis of IIM (IIM group) while 51 (60%) received an alternative diagnosis (non-IIM group). A cutoff score ≥ 18 was able to correctly classify patients having an IIM with an area under the curve of 0.85, specificity of 96%, and sensitivity of 52.9%., Conclusion: Our study demonstrates that a quantitative MRI score for muscle edema in the lower limbs (thighs and legs) aids in distinguishing IIM from conditions that mimic it., (© 2024. The Author(s).)

Background: Patients with suspected ramp lesions on magnetic resonance imaging (MRI) or ultrasonography (US) healed and showed no instability based on intraoperative arthroscopic findings. The purpose of this study was to assess the use of US in evaluating ramp lesions preoperatively and intraoperatively., Methods: Eighty-two knees that underwent anterior cruciate ligament (ACL) reconstruction between January 2022 and June 2023 were included to assess the ramp lesion complication rate and instability using arthroscopic findings. The detection rate of ramp lesions using US at the initial visit and preoperatively was also investigated. The test-retest reliability was assessed using the intraclass correlation coefficient and analyzed using two-way random effects and absolute agreement. The patients were divided into two groups based on the presence or absence of ramp lesions, and these data were compared using Student's t-test. Statistical significance was set at p < 0.05., Results: On ultrasound examination, 90.0% of the cases had a ramp lesion at the initial examination, of which 22.2% were poorly delineated on the day of surgery. In the cases where the ramp lesion was unstable at the time of surgery, it could be delineated using US. In the cases where the ramp lesion was stable, it was difficult to delineate the lesion using US., Conclusions: Unstable ramp lesions complicating ACL injuries could be detected using US., (© 2024. The Author(s), under exclusive licence to The Japan Society of Ultrasonics in Medicine.)

Background: Magnetic resonance imaging [MRI] findings in Neuromyelitis optica spectrum disorder [NMOSD] and Multiple Sclerosis [MS] patients could lead us to discriminate toward them. For instance, U-fiber and Dawson's finger-type lesions are suggestive of MS, however linear ependymal lesions raise the possibility of NMOSD. Recently, artificial intelligence [AI] models have been used to discriminate between NMOSD and MS based on MRI features. In this study, we aim to systematically review the capability of AI algorithms in NMOSD and MS discrimination based on MRI features., Method: We searched PubMed, Scopus, Web of Sciences, Embase, and IEEE databases up to August 2023. All studies that used AI-based algorithms to discriminate between NMOSD and MS using MRI features were included, without any restriction in time, region, race, and age. Data on NMOSD and MS patients, Aquaporin-4 antibodies [AQP4-Ab] status, diagnosis criteria, performance metrics (accuracy, sensitivity, specificity, and AUC), artificial intelligence paradigm, MR imaging, and used features were extracted. This study is registered with PROSPERO, CRD42023465265., Results: Fifteen studies were included in this systematic review, with sample sizes ranging between 53 and 351. 1,362 MS patients and 1,118 NMOSD patients were included in our systematic review. AQP4-Ab was positive in 94.9% of NMOSD patients in 9 studies. Eight studies used machine learning [ML] as a classifier, while 7 used deep learning [DL]. AI models based on only MRI or MRI and clinical features yielded a pooled accuracy of 82% (95% CI: 78-86%), sensitivity of 83% (95% CI: 79-88%), and specificity of 80% (95% CI: 75-86%). In subgroup analysis, using only MRI features yielded an accuracy, sensitivity, and specificity of 83% (95% CI: 78-88%), 81% (95% CI: 76-87%), and 84% (95% CI: 79-89%), respectively., Conclusion: AI models based on MRI features showed a high potential to discriminate between NMOSD and MS. However, heterogeneity in MR imaging, model evaluation, and reporting performance metrics, among other confounders, affected the reliability of our results. Well-designed studies on multicentric datasets, standardized imaging and evaluation protocols, and detailed transparent reporting of results are needed to reach optimal performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurological disease and expanding our knowledge of the brain. However, there are many challenges inherent in managing and analyzing MRI data, due in large part to the heterogeneity of data acquisition. To address this, we have developed MRIO, the Magnetic Resonance Imaging Acquisition and Analysis Ontology. MRIO provides well-reasoned classes and logical axioms for the acquisition of several MRI acquisition types and well-known, peer-reviewed analysis software, facilitating the use of MRI data. These classes provide a common language for the neuroimaging research process and help standardize the organization and analysis of MRI data for reproducible datasets. We also provide queries for automated assignment of analyses for given MRI types. MRIO aids researchers in managing neuroimaging studies by helping organize and annotate MRI data and integrating with existing standards such as Digital Imaging and Communications in Medicine and the Brain Imaging Data Structure, enhancing reproducibility and interoperability. MRIO was constructed according to Open Biomedical Ontologies Foundry principles and has contributed several classes to the Ontology for Biomedical Investigations to help bridge neuroimaging data to other domains. MRIO addresses the need for a "common language" for MRI that can help manage the neuroimaging research, by enabling researchers to identify appropriate analyses for sets of scans and facilitating data organization and reporting., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background Context: No method currently exists for MRI-based determination of ossification of the posterior longitudinal ligament (OPLL) of the cervical spine using objective criteria., Purpose: The purpose of this study was to develop an MRI-based score to determine whether a lesion represents a cervical OPLL lesion and to establish the objective diagnostic value., Study Design: Retrospective cohort in a single medical institution., Patient Sample: Thirty-five patients undergoing surgery for OPLL (Group A) and 99 patients undergoing cervical disc arthroplasty for soft disc herniation (Group B) between 2011 and 2020 were retrospectively included. All OPLL lesions on unenhanced MRI scan were correlated with a corresponding CT scan. Demographics were comparable between the two groups., Outcome Measures (physiologic Measures): Using unenhanced magnetic resonance imaging (MRI), the T1- and T2- lesion quality (LQ) scores were calculated. Receiver operating characteristic (ROC) analysis was performed to calculate the area-under-the-curve (AUC) of both LQ scores as a predictor of the presence of OPLL. Computed tomography (CT)-based Hounsfield unit (HU) values of OPLL lesions were obtained and compared with both LQ scores. The LQ scores for MRI scanners from different manufacturers were compared using Student's t test to confirm the validity of the LQ score by scanner type., Methods: The regions of interest for signal intensity (SI) were defined as the darkest site of the lesion and the cerebrospinal fluid (CSF) at the cerebellomedullary cistern. The T1 and T2 LQ scores were measured as the ratio of the SI at the darkest site of the lesion divided by the SI of the CSF., Results: The T1 and T2 LQ scores in Group A were significantly lower than those in Group B (p<.001). ROC analysis determined that T1 and T2 LQ scores of 0.46 and 0.07, respectively, could distinguish the presence of OPLL with an accuracy of 0.93 and 0.89, respectively (p<.001). When the T1 LQ score of the lesion is <0.46, a diagnosis of OPLL may be suspected with 100% sensitivity and 92.3% specificity. The HU of the lesion had a moderate negative correlation with the T1 LQ score (r=-0.665, p<.0001). Both LQ scores were unaffected by manufacturer type., Conclusions: This study found a correlation between the MRI-based T1 LQ scores and CT-based HU value for identifying OPLL lesions. Additional studies will be needed to validate that the T1 LQ score from the unenhanced MRI scan can identify cervical OPLL., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy., Competing Interests: Declaration of Competing Interest All authors declared no conflict of interest in this work., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective. Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment. Approach. A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre's clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers. Main results . Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal. Significance . The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies., (Creative Commons Attribution license.)

Purpose To provide a comprehensive head-to-head comparison and temporal analysis of cardiac MRI indications between the European Society of Cardiology (ESC) and American College of Cardiology/American Heart Association (ACC/AHA) guidelines to identify areas of consensus and divergence. Materials and Methods A systematic review and meta-analysis was conducted. ESC and ACC/AHA guidelines published until May 2023 were systematically screened for recommendations related to cardiac MRI. The class of recommendation (COR) and level of evidence (LOE) for cardiac MRI recommendations were compared between the two guidelines and between newer versus older versions of each guideline using χ 2 or Fisher exact tests. Results ESC guidelines included 109 recommendations regarding cardiac MRI, and ACC/AHA guidelines included 90 recommendations. The proportion of COR I and LOE B was higher in ACC/AHA versus ESC guidelines (60% [54 of 90] vs 46.8% [51 of 109]; P = .06 and 53% [48 of 90] vs 35.8% [39 of 109], respectively; P = .01). The increase in the number of cardiac MRI recommendations over time was significantly higher in ESC guidelines (from 63 to 109 for ESC vs from 65 to 90 for ACC/AHA; P = .03). The main areas of consensus were found in heart failure and hypertrophic cardiomyopathy, while the main divergences were in valvular heart disease, arrhythmias, and aortic disease. Conclusion ESC guidelines included more recommendations related to cardiac MRI use, whereas the ACC/AHA recommendations had higher COR and LOE. The number of cardiac MRI recommendations increased significantly over time in both guidelines, indicating the increasing role of cardiac MRI evaluation and management of cardiovascular disease. Keywords: Cardiovascular Magnetic Resonance, Guideline, European Society of Cardiology, ESC, American College of Cardiology/American Heart Association, ACC/AHA Supplemental material is available for this article. © RSNA, 2024.

Background: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes. Real-time quality assurance by humans is a complex task requiring training and experience, so being able to identify which images have an intermediate likelihood of LGE while the scan is ongoing, without the presence of an expert is of high value. This decision-support could prompt immediate image optimization or acquisition of supplementary images to confirm or refute the presence of genuine LGE. This could reduce ambiguity in reports., Methods: Short-axis, phase-sensitive inversion recovery late gadolinium images were extracted from our clinical cardiac magnetic resonance (CMR) database and shuffled. Two, independent, blinded experts scored each individual slice for "LGE likelihood" on a visual analog scale, from 0 (absolute certainty of no LGE) to 100 (absolute certainty of LGE), with 50 representing clinical equipoise. The scored images were split into two classes-either "high certainty" of whether LGE was present or not, or "low certainty." The dataset was split into training, validation, and test sets (70:15:15). A deep learning binary classifier based on the EfficientNetV2 convolutional neural network architecture was trained to distinguish between these categories. Classifier performance on the test set was evaluated by calculating the accuracy, precision, recall, F1-score, and area under the receiver operating characteristics curve (ROC AUC). Performance was also evaluated on an external test set of images from a different center., Results: One thousand six hundred and forty-five images (from 272 patients) were labeled and split at the patient level into training (1151 images), validation (247 images), and test (247 images) sets for the deep learning binary classifier. Of these, 1208 images were "high certainty" (255 for LGE, 953 for no LGE), and 437 were "low certainty". An external test comprising 247 images from 41 patients from another center was also employed. After 100 epochs, the performance on the internal test set was accuracy = 0.94, recall = 0.80, precision = 0.97, F1-score = 0.87, and ROC AUC = 0.94. The classifier also performed robustly on the external test set (accuracy = 0.91, recall = 0.73, precision = 0.93, F1-score = 0.82, and ROC AUC = 0.91). These results were benchmarked against a reference inter-expert accuracy of 0.86., Conclusion: Deep learning shows potential to automate quality control of late gadolinium imaging in CMR. The ability to identify short-axis images with intermediate LGE likelihood in real-time may serve as a useful decision-support tool. This approach has the potential to guide immediate further imaging while the patient is still in the scanner, thereby reducing the frequency of recalls and inconclusive reports due to diagnostic indecision., Competing Interests: Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients., Methods: Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated., Results: For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D., Conclusion: This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies., (© 2024 The Authors. Brain and Behavior published by Wiley Periodicals LLC.)

Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Background: Visualizing edges is critical for neuroimaging. For example, edge maps enable quality assurance for the automatic alignment of an image from one modality (or individual) to another., New Method: We suggest that using the second derivative (difference of Gaussian, or DoG) provides robust edge detection. This method is tuned by size (which is typically known in neuroimaging) rather than intensity (which is relative)., Results: We demonstrate that this method performs well across a broad range of imaging modalities. The edge contours produced consistently form closed surfaces, whereas alternative methods may generate disconnected lines, introducing potential ambiguity in contiguity., Comparison With Existing Methods: Current methods for computing edges are based on either the first derivative of the image (FSL), or a variation of the Canny Edge detection method (AFNI). These methods suffer from two primary limitations. First, the crucial tuning parameter for each of these methods relates to the image intensity. Unfortunately, image intensity is relative for most neuroimaging modalities making the performance of these methods unreliable. Second, these existing approaches do not necessarily generate a closed edge/surface, which can reduce the ability to determine the correspondence between a represented edge and another image., Conclusion: The second derivative is well suited for neuroimaging edge detection. We include this method as part of both the AFNI and FSL software packages, standalone code and online., Competing Interests: Declaration of Competing Interest The authors do not have any conflicts of interests that might appear to affect their ability to present data objectively. These include relevant financial (for example patent ownership, stock ownership, consultancies, speaker's fees), personal, political, intellectual, or religious interests. This work was supported by grants, as listed in the acknowledgments section., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This position statement guides cardiovascular magnetic resonance (CMR) imaging program directors and learners on the key competencies required for Level II and III CMR practitioners, whether trainees come from a radiology or cardiology background. This document is built upon existing curricula and was created and vetted by an international panel of cardiologists and radiologists on behalf of the Society for Cardiovascular Magnetic Resonance (SCMR)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Cardiovascular magnetic resonance (CMR) is a proven imaging modality for informing diagnosis and prognosis, guiding therapeutic decisions, and risk stratifying surgical intervention. Patients with a cardiac implantable electronic device (CIED) would be expected to derive particular benefit from CMR given high prevalence of cardiomyopathy and arrhythmia. While several guidelines have been published over the last 16 years, it is important to recognize that both the CIED and CMR technologies, as well as our knowledge in MR safety, have evolved rapidly during that period. Given increasing utilization of CIED over the past decades, there is an unmet need to establish a consensus statement that integrates latest evidence concerning MR safety and CIED and CMR technologies. While experienced centers currently perform CMR in CIED patients, broad availability of CMR in this population is lacking, partially due to limited availability of resources for programming devices and appropriate monitoring, but also related to knowledge gaps regarding the risk-benefit ratio of CMR in this growing population. To address the knowledge gaps, this SCMR Expert Consensus Statement integrates consensus guidelines, primary data, and opinions from experts across disparate fields towards the shared goal of informing evidenced-based decision-making regarding the risk-benefit ratio of CMR for patients with CIEDs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework., (© 2023. The Author(s).)

Background: The revised Lublin classification offers a framework for categorizing multiple sclerosis (MS) according to the clinical course and imaging results. Diagnosis of secondary progressive MS (SPMS) is often delayed by a period of uncertainty. Several quantitative magnetic resonance imaging (qMRI) markers are associated with progressive disease states, but they are not usually available in clinical practice., Methods: The MAGNON project enrolled 629 patients (early relapsing-remitting MS (RRMS), n = 51; RRMS with suspected SPMS, n = 386; SPMS, n = 192) at 55 centers in Germany. Routine magnetic resonance imaging (MRI) scans at baseline and after 12 months were analyzed using a centralized automatic processing pipeline to quantify lesions and normalized brain and thalamic volume. Clinical measures included relapse activity, disability, and MS phenotyping. Neurologists completed questionnaires before and after receiving the qMRI reports., Results: According to the physicians' reports, qMRI results changed their assessment of the patient in 31.8% (baseline scan) and 27.6% (follow-up scan). For ∼50% of patients with RRMS with suspected SPMS, reports provided additional information that the patient was transitioning to SPMS. In >25% of all patients, this information influenced the physicians' assessment of the patient's current phenotype. However, actual changes of treatment were reported only in a minority of these patients., Conclusions: The MAGNON results suggest that standardized qMRI reports may be integrated into the routine clinical care of MS patients and support the application of the Lublin classification as well as treatment decisions. The highest impact was reported in patients with suspected SPMS, indicating a potential to reduce diagnostic uncertainty., (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions., (© 2024 The Authors. Hippocampus published by Wiley Periodicals LLC.)